How to Use the Cloudbeds MCP in Pydantic AI

Use Pydantic AI with Cloudbeds to enforce strict data validation on every reservation and guest record.

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Connect Cloudbeds MCP to Pydantic AI

Create your Vinkius account to connect Cloudbeds to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cloudbeds with Pydantic AI

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Type-safe Cloudbeds tools for Pydantic AI

Every tool response from `list_room_types` or `get_reservation` is validated against your Pydantic schemas. If the API returns a malformed date or missing field, the agent raises a validation error immediately. This prevents your agent from making decisions based on bad data. You get a reliable interface between your hotel operations and your agent's reasoning engine.

Financial tracking with Pydantic AI and Cloudbeds

Use `list_transactions` to pull guest financial history into your agent. Pydantic AI ensures the currency amounts and reservation IDs match your expected types before the agent processes them. Your code remains clean because you don't have to write custom validation logic for every API call. The MCP Server provides the data, and your models enforce the structure.

Automated guest searches via Pydantic AI

Run `search_guests` to fetch detailed profiles that conform to your strict type definitions. The agent receives a structured object containing nationality, contact info, and lifetime value. This structure allows your agent to handle guest inquiries with precision. If the data doesn't fit your model, Pydantic AI catches the discrepancy, protecting your system from corrupted state.

Setup guide

Set up Cloudbeds MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "cloudbeds-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Cloudbeds tools.",
)

result = await agent.run("List recent Cloudbeds transactions")
print(result.output)

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Common questions about Cloudbeds MCP in Pydantic AI

You define your classes using standard Pydantic syntax. When the MCP Server returns data, the framework automatically validates it against your schema.

Yes, you use the MCPToolset class to connect the server. It supports SSE and HTTP transports, which you configure during your agent's setup.

The agent will fail with a validation error rather than proceeding with incorrect data. This prevents silent corruption of your reservation records.

Yes, the framework is model-agnostic. You can pair this server with local models or OpenAI, provided you have your Pydantic schemas defined correctly.

The server transmits data directly into your validated local objects. The memory is cleared after the agent cycle, ensuring sensitive transaction details are not cached.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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